repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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NMTGMinor | NMTGMinor-master/onmt/modules/optimized/compat.py | import torch
import functools
def custom_fwd(fwd=None, **kwargs):
"""
Helper decorator for ``forward`` methods of custom autograd functions (subclasses of
:class:`torch.autograd.Function`). See the :ref:`example page<amp-custom-examples>` for more detail.
Arguments:
cast_inputs (:class:`torc... | 2,177 | 31.507463 | 106 | py |
NMTGMinor | NMTGMinor-master/onmt/modules/optimized/fused_adam.py | import torch
class MultiTensorApply(object):
available = False
warned = False
def __init__(self, chunk_size):
try:
import fused_optim
MultiTensorApply.available = True
self.chunk_size = chunk_size
except ImportError as err:
MultiTensorApply.... | 8,746 | 44.557292 | 145 | py |
NMTGMinor | NMTGMinor-master/onmt/modules/optimized/encdec_attention_func.py | """
Encoder-Decoder multi-head attention.
Code is heavily adapted from apex
https://github.com/NVIDIA/apex/tree/master/apex/contrib/csrc/multihead_attn
"""
import torch
import torch.nn.functional as F
try:
from torch.cuda.amp import custom_fwd, custom_bwd
except (ModuleNotFoundError, ImportError) as e:
from .... | 36,308 | 46.964333 | 120 | py |
NMTGMinor | NMTGMinor-master/onmt/modules/optimized/fast_mha.py | ###############################################################################
# Copyright (c) 2011-2021, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * Redistribution... | 7,225 | 40.768786 | 118 | py |
NMTGMinor | NMTGMinor-master/onmt/modules/optimized/flash_mha.py | ###############################################################################
# Copyright (c) 2011-2021, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * Redistribution... | 11,249 | 44.731707 | 101 | py |
NMTGMinor | NMTGMinor-master/onmt/modules/optimized/rotary_encodings.py | import torch
import torch.nn.functional as F
from einops import rearrange, repeat
class SinusoidalEmbeddings(torch.nn.Module):
def __init__(self, dim):
super().__init__()
inv_freq = 1. / (10000 ** (torch.arange(0, dim, 2).float() / dim))
self.register_buffer('inv_freq', inv_freq)
def ... | 1,081 | 30.823529 | 80 | py |
NMTGMinor | NMTGMinor-master/onmt/modules/optimized/relative_self_attention.py | import math
import torch
from torch import nn
from torch.nn import Parameter
import torch.nn.functional as F
from .relative_self_attention_func import relative_self_attn_func
from .relative_self_attention_func import RelativeShift
import onmt
class RelativeSelfMultiheadAttn(nn.Module):
"""Multi-headed attention... | 10,003 | 42.307359 | 119 | py |
NMTGMinor | NMTGMinor-master/onmt/modules/optimized/self_attention_func.py | """
Self-attention with multi-head attention.
Code is taken from apex self-attention implementation
https://github.com/NVIDIA/apex/tree/master/apex/contrib/csrc/multihead_attn
"""
import torch
import torch.nn.functional as F
try:
from torch.cuda.amp import custom_fwd, custom_bwd
except (ModuleNotFoundError, Impor... | 43,104 | 44.421496 | 130 | py |
NMTGMinor | NMTGMinor-master/onmt/modules/optimized/encdec_attention.py | import math
import torch
from torch import nn
from torch.nn import Parameter
import torch.nn.functional as F
from .encdec_attention_func import encdec_attn_func
import onmt
class EncdecMultiheadAttn(nn.Module):
"""Multi-headed encoder-decoder attention.
See "Attention Is All You Need" for more details.
""... | 6,634 | 41.261146 | 111 | py |
NMTGMinor | NMTGMinor-master/onmt/modules/extensions/setup.py | import torch
from torch.utils import cpp_extension
from setuptools import setup, find_packages
import subprocess
import sys
import warnings
import os
from torch.utils.cpp_extension import CUDAExtension
from torch.utils.cpp_extension import BuildExtension
from torch.utils.cpp_extension import CUDA_HOME
# ninja build ... | 17,708 | 51.862687 | 122 | py |
NMTGMinor | NMTGMinor-master/onmt/modules/extensions/setup_base.py | import torch
from torch.utils import cpp_extension
from setuptools import setup, find_packages
import subprocess
import sys
import warnings
import os
from torch.utils.cpp_extension import CUDAExtension
from torch.utils.cpp_extension import BuildExtension
from torch.utils.cpp_extension import CUDA_HOME
# ninja build ... | 15,451 | 50.165563 | 122 | py |
NMTGMinor | NMTGMinor-master/onmt/modules/extensions/flashattn/setup.py | import torch
from torch.utils import cpp_extension
from setuptools import setup, find_packages
import subprocess
from pathlib import Path
import sys
import warnings
import os
from torch.utils.cpp_extension import CUDAExtension
from torch.utils.cpp_extension import BuildExtension
from torch.utils.cpp_extension import ... | 6,031 | 34.904762 | 101 | py |
NMTGMinor | NMTGMinor-master/onmt/modules/extensions/mlp_blaslt/test_fused_dense.py | from copy import copy, deepcopy
import math
import torch
from torch import nn
import torch.nn.functional as F
import unittest
from time import time
import numpy as np
import random
try:
from torch.cuda.amp import custom_fwd, custom_bwd
except (ModuleNotFoundError, ImportError) as e:
from ..optimized.compat imp... | 20,463 | 37.466165 | 122 | py |
NMTGMinor | NMTGMinor-master/onmt/modules/extensions/mlp_blaslt/setup.py | import torch
from torch.utils.cpp_extension import BuildExtension, CppExtension, CUDAExtension, CUDA_HOME
from setuptools import setup, find_packages
import subprocess
import sys
import warnings
import os
# ninja build does not work unless include_dirs are abs path
this_dir = os.path.dirname(os.path.abspath(__file__)... | 5,888 | 45.738095 | 277 | py |
NMTGMinor | NMTGMinor-master/onmt/modules/extensions/mlp_blaslt/test_linear.py | from copy import copy, deepcopy
import math
import torch
from torch import nn
import torch.nn.functional as F
import unittest
from time import time
import numpy as np
import random
from torch import Tensor
try:
from torch.cuda.amp import custom_fwd, custom_bwd
except (ModuleNotFoundError, ImportError) as e:
fr... | 11,042 | 36.181818 | 119 | py |
NMTGMinor | NMTGMinor-master/onmt/modules/extensions/blru/blru.py | import sys
python = sys.argv[1]=="0"
import time
if not python:
from torch.utils.cpp_extension import load
blru = load(name="blru", sources=["blru.cpp","blru_kernel.cu"]) #, verbose=True)
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import math
from torch.cuda.amp imp... | 6,085 | 34.383721 | 113 | py |
NMTGMinor | NMTGMinor-master/onmt/modules/extensions/multihead_attn/setup.py | import torch
from torch.utils import cpp_extension
from setuptools import setup, find_packages
import subprocess
import sys
import warnings
import os
from torch.utils.cpp_extension import CUDAExtension
from torch.utils.cpp_extension import BuildExtension
def get_cuda_bare_metal_version(cuda_dir):
raw_output = s... | 5,186 | 42.588235 | 98 | py |
NMTGMinor | NMTGMinor-master/onmt/modules/extensions/layer_norm/test_layer_norm.py | from copy import copy, deepcopy
import math
import torch
from torch import nn
import torch.nn.functional as F
import unittest
from time import time
import numpy as np
import random
import fast_layer_norm_cuda
def _cast_if_autocast_enabled(*args):
if not torch.is_autocast_enabled():
return args
else:... | 7,215 | 31.071111 | 130 | py |
NMTGMinor | NMTGMinor-master/onmt/modules/extensions/multihead_attn_blaslt/setup.py | import torch
from torch.utils import cpp_extension
from setuptools import setup, find_packages
import subprocess
import sys
import warnings
import os
from torch.utils.cpp_extension import CUDAExtension
from torch.utils.cpp_extension import BuildExtension
from torch.utils.cpp_extension import CUDA_HOME
# ninja build ... | 5,264 | 39.19084 | 101 | py |
NMTGMinor | NMTGMinor-master/onmt/modules/extensions/mlp/mlp_gelu_dropoutadd.py | from copy import copy, deepcopy
import math
import torch
from torch import nn
import torch.nn.functional as F
import unittest
from time import time
import numpy as np
import random
import silu_cuda
try:
import apex.amp as amp
from apex.amp import half_function
except (ModuleNotFoundError, ImportError) as e:
... | 11,654 | 32.491379 | 122 | py |
NMTGMinor | NMTGMinor-master/onmt/modules/batch_ensemble/be_relative_attention.py | import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.nn import Parameter
import math
class BatchEnsembleMM(object):
@staticmethod
def forward(x, weight, bias, ensemble_r, ensemble_s):
"""
:param x: [T x B x H]
:param weight: [H_out x H]
:param bias: [H... | 34,591 | 45 | 119 | py |
NMTGMinor | NMTGMinor-master/onmt/modules/batch_ensemble/be_encdec_attention.py | import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.nn import Parameter
import math
# from onmt.constants import double_precision
# from .batch_ensemble_linear import BatchEnsembleMM as mm
class BatchEnsembleMM(object):
@staticmethod
def forward(x, weight, bias, ensemble_r, ensembl... | 26,603 | 46.677419 | 117 | py |
NMTGMinor | NMTGMinor-master/onmt/modules/batch_ensemble/be_feed_forward.py | 0 | 0 | 0 | py | |
NMTGMinor | NMTGMinor-master/onmt/modules/batch_ensemble/__init__.py | 0 | 0 | 0 | py | |
NMTGMinor | NMTGMinor-master/onmt/modules/batch_ensemble/batch_ensemble_linear.py | import torch
import torch.nn.functional as F
from onmt.modules.dropout import variational_dropout
class BatchEnsembleMM(object):
@staticmethod
def forward(x, weight, bias, ensemble_r, ensemble_s):
"""
:param x: [T x B x H]
:param weight: [H_out x H]
:param bias: [H_out]
... | 12,098 | 38.15534 | 109 | py |
NMTGMinor | NMTGMinor-master/onmt/modules/bayes_by_backprop/embedding.py | 0 | 0 | 0 | py | |
NMTGMinor | NMTGMinor-master/onmt/modules/bayes_by_backprop/utils.py | import torch
def flatten_list(tensors):
flat = list()
indices = list()
shapes = list()
s = 0
for tensor in tensors:
shapes.append(tensor.shape)
flat_t = torch.flatten(tensor)
size = flat_t.shape[0]
flat.append(flat_t)
indices.append((s, s+size))
s... | 599 | 18.354839 | 46 | py |
NMTGMinor | NMTGMinor-master/onmt/modules/bayes_by_backprop/feed_forward.py | import math
import torch
from torch import nn
from torch.nn import Parameter
import torch.nn.functional as F
from onmt.modules.dropout import variational_dropout
from .gaussian import Gaussian, ScaleMixtureGaussian
from .utils import flatten_list, unflatten
class PositionWiseFeedForward(nn.Module):
"""Multi-head... | 4,046 | 37.913462 | 112 | py |
NMTGMinor | NMTGMinor-master/onmt/modules/bayes_by_backprop/gaussian.py | import torch
import torch.nn.functional as F
import numpy
import math
import torch.nn as nn
log_sqrt_2pi = math.log(math.sqrt(2 * math.pi))
class Gaussian(object):
def __init__(self, mu, rho):
super().__init__()
self.mu = mu
self.rho = rho
self.normal = torch.distributions.Normal(... | 2,619 | 33.473684 | 112 | py |
NMTGMinor | NMTGMinor-master/onmt/modules/bayes_by_backprop/__init__.py | 0 | 0 | 0 | py | |
NMTGMinor | NMTGMinor-master/onmt/modules/bayes_by_backprop/generator.py | class Generator(nn.Module):
def __init__(self, hidden_size, output_size, fix_norm=False):
super(Generator, self).__init__()
self.hidden_size = hidden_size
self.output_size = output_size
self.linear = nn.Linear(hidden_size, output_size)
self.fix_norm = fix_norm
stdv... | 1,214 | 31.837838 | 77 | py |
NMTGMinor | NMTGMinor-master/onmt/modules/bayes_by_backprop/relative_self_attention.py | import math
import torch
from torch import nn
from torch.nn import Parameter
import torch.nn.functional as F
from .gaussian import Gaussian, ScaleMixtureGaussian
from .utils import flatten_list, unflatten
from ..optimized.relative_self_attention_func import relative_self_attn_func
# from .fast_self_multihead_attn_fun... | 5,261 | 42.131148 | 112 | py |
NMTGMinor | NMTGMinor-master/onmt/modules/bayes_by_backprop/encdec_attention.py | import math
import torch
from torch import nn
from torch.nn import Parameter
import torch.nn.functional as F
from ..optimized.encdec_attention_func import encdec_attn_func
from .gaussian import Gaussian, ScaleMixtureGaussian
from .utils import flatten_list, unflatten
class EncdecMultiheadAttn(nn.Module):
"""Multi... | 5,175 | 42.133333 | 117 | py |
NMTGMinor | NMTGMinor-master/onmt/modules/kernels/__init__.py | 0 | 0 | 0 | py | |
NMTGMinor | NMTGMinor-master/onmt/modules/kernels/kernel.py |
"""Construct wide convolution kernels."""
from typing import Optional, Mapping, Tuple, Union
from collections import defaultdict
import math
import torch
import torch.nn as nn
| 180 | 15.454545 | 50 | py |
NMTGMinor | NMTGMinor-master/onmt/modules/mlp/test_mlp_gelu.py | from copy import copy, deepcopy
import math
import torch
from torch import nn
import torch.nn.functional as F
import unittest
from time import time
import numpy as np
import random
try:
from torch.cuda.amp import custom_fwd, custom_bwd
except (ModuleNotFoundError, ImportError) as e:
from ..optimized.compat imp... | 22,661 | 38.005164 | 122 | py |
NMTGMinor | NMTGMinor-master/onmt/modules/mlp/mlp.py | from copy import copy
import math
import torch
from torch import nn
import unittest
from time import time
import numpy as np
try:
from torch.cuda.amp import custom_fwd, custom_bwd
except (ModuleNotFoundError, ImportError) as e:
from ..optimized.compat import custom_fwd, custom_bwd
try:
import fused_mlp_re... | 15,995 | 33.252677 | 119 | py |
NMTGMinor | NMTGMinor-master/onmt/modules/mlp/test_mlp_relu.py | from copy import copy, deepcopy
import math
import torch
from torch import nn
import torch.nn.functional as F
import unittest
from time import time
import numpy as np
import random
try:
from torch.cuda.amp import custom_fwd, custom_bwd
except (ModuleNotFoundError, ImportError) as e:
from ..optimized.compat imp... | 13,167 | 36.409091 | 122 | py |
NMTGMinor | NMTGMinor-master/onmt/modules/mlp/__init__.py | from .mlp import *
| 19 | 9 | 18 | py |
NMTGMinor | NMTGMinor-master/onmt/models/transformer_xl.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from onmt.modules.relative_attention import RelPartialLearnableMultiHeadAttn
from onmt.models.transformer_layers import PositionalEncoding, PrePostProcessing
from onmt.models.transformer_layers import EncoderLayer, DecoderLayer
from onmt.models.transfor... | 9,715 | 36.513514 | 120 | py |
NMTGMinor | NMTGMinor-master/onmt/models/transformers.py | import copy
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import defaultdict
from torch.utils.checkpoint import checkpoint
import onmt
from onmt.models.transformer_layers import EncoderLayer, DecoderLayer, PositionalEncoding, \
PrePostProcessing
... | 40,743 | 39.18146 | 118 | py |
NMTGMinor | NMTGMinor-master/onmt/models/performer_layer.py | import torch
def softmax_kernel(data, *, projection_matrix, is_query, normalize_data=True, eps=1e-4, device=None):
b, h, *_ = data.shape
data_normalizer = (data.shape[-1] ** -0.25) if normalize_data else 1.
ratio = (projection_matrix.shape[0] ** -0.5)
projection = repeat(projection_matrix, 'j d -> ... | 9,531 | 35.945736 | 136 | py |
NMTGMinor | NMTGMinor-master/onmt/models/transformer_layers.py | import math
import torch
import torch.nn as nn
import torch.nn.init as init
import torch.nn.utils.weight_norm as WeightNorm
import onmt
import torch.nn.functional as F
from onmt.modules.bottle import Bottle
from onmt.modules.static_dropout import StaticDropout
from onmt.modules.linear import XavierLinear as Linear
from... | 17,782 | 39.142212 | 118 | py |
NMTGMinor | NMTGMinor-master/onmt/models/pretrain_transformer.py | import copy
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import defaultdict
from torch.utils.checkpoint import checkpoint
import onmt
from onmt.modules.base_seq2seq import NMTModel, Reconstructor, DecoderState
from onmt.models.transformers import Tr... | 18,440 | 45.923664 | 118 | py |
NMTGMinor | NMTGMinor-master/onmt/models/__init__.py | 0 | 0 | 0 | py | |
NMTGMinor | NMTGMinor-master/onmt/models/relative_transformer.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from onmt.models.transformer_layers import PositionalEncoding, PrePostProcessing
from onmt.models.transformer_layers import EncoderLayer, DecoderLayer
from onmt.models.transformers import TransformerEncoder, TransformerDecoder, Transformer, Transformer... | 49,047 | 40.11316 | 119 | py |
NMTGMinor | NMTGMinor-master/onmt/models/relative_transformer_layers.py | import torch
import torch.nn as nn
import onmt
from onmt.models.transformer_layers import PrePostProcessing, MultiHeadAttention, Linear
from onmt.modules.relative_attention import RelPartialLearnableMultiHeadAttn
from onmt.modules.optimized.relative_self_attention import RelativeSelfMultiheadAttn
from onmt.utils impor... | 10,207 | 44.775785 | 120 | py |
NMTGMinor | NMTGMinor-master/onmt/models/speech_recognizer/lstm.py | import random
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
from collections import defaultdict
import math
import onmt
from onmt.modules.base_seq2seq import NMTModel, DecoderState
from onmt.models.transformer_layers import P... | 30,801 | 39.002597 | 118 | py |
NMTGMinor | NMTGMinor-master/onmt/models/speech_recognizer/perceiver.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from onmt.models.transformer_layers import PositionalEncoding
from onmt.modules.pre_post_processing import PrePostProcessing
from onmt.models.transformers import TransformerEncoder, TransformerDecoder, Transformer, TransformerDecodingState
from onmt.mo... | 3,242 | 42.24 | 120 | py |
NMTGMinor | NMTGMinor-master/onmt/models/speech_recognizer/classifier.py | import copy
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import defaultdict
class TransformerClassifier(nn.Module):
"""Main model in 'Attention is all you need' """
def __init__(self, encoder, generator=None, mpc=False, **kwargs):
... | 2,848 | 30.655556 | 107 | py |
NMTGMinor | NMTGMinor-master/onmt/models/speech_recognizer/lid_loss.py | import math
import numpy
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
import onmt
import onmt.modules
from onmt.utils import flip
class CrossEntropyLIDLoss(_Loss):
"""
Class for managing efficient loss computation.
loss computations
Users ... | 3,563 | 32.308411 | 108 | py |
NMTGMinor | NMTGMinor-master/onmt/models/speech_recognizer/conformer.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from onmt.models.transformers import TransformerEncoder, TransformerDecoder, Transformer, TransformerDecodingState
from onmt.modules.sinusoidal_positional_encoding import SinusoidalPositionalEmbedding
import onmt
from onmt.modules.base_seq2seq import N... | 12,149 | 37.571429 | 126 | py |
NMTGMinor | NMTGMinor-master/onmt/models/speech_recognizer/wav2vec2.py | import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from onmt.models.transformers import Transformer, TransformerDecodingState
from typing import List, Optional, Union
from collections import defaultdict
import onmt
from onmt.modules.optimized.linear import Linear
import math
from .fairseq_wav2... | 47,708 | 43.839286 | 123 | py |
NMTGMinor | NMTGMinor-master/onmt/models/speech_recognizer/wavlm.py | import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from onmt.models.transformers import Transformer, TransformerDecodingState
from typing import List, Optional, Union
from collections import defaultdict
import onmt
from onmt.modules.optimized.linear import Linear
import math
from .fairseq_wav2... | 8,507 | 40.300971 | 119 | py |
NMTGMinor | NMTGMinor-master/onmt/models/speech_recognizer/__init__.py | 0 | 0 | 0 | py | |
NMTGMinor | NMTGMinor-master/onmt/models/speech_recognizer/conformer_layers.py | import torch
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
import math
from onmt.modules.optimized.relative_self_attention import RelativeSelfMultiheadAttn
from onmt.modules.optimized.feed_forward import PositionWiseFeedForward
from onmt.modules.dropout import variational_dropout
f... | 6,311 | 43.450704 | 109 | py |
NMTGMinor | NMTGMinor-master/onmt/models/speech_recognizer/relative_transformer.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from onmt.models.transformer_layers import PositionalEncoding
from onmt.modules.pre_post_processing import PrePostProcessing
from onmt.models.transformers import TransformerEncoder, TransformerDecoder, Transformer, TransformerDecodingState
from onmt.mo... | 28,183 | 42.293395 | 120 | py |
NMTGMinor | NMTGMinor-master/onmt/models/speech_recognizer/relative_transformer_layers.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import onmt
from onmt.modules.pre_post_processing import PrePostProcessing
from onmt.modules.linear import FeedForward
from onmt.modules.attention import MultiHeadAttention
from onmt.modules.dropout import VariationalDropout
from onmt.modules.optimize... | 24,391 | 52.026087 | 127 | py |
NMTGMinor | NMTGMinor-master/onmt/models/speech_recognizer/mssm/mhs4.py | #!/usr/bin/env python3
from typing import Optional, List, Tuple, Union
import time
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.init as init
import torch.optim as optim
import torch.nn.functional as F
from torch import Tensor
# import pykeops
# import pykeops.torch
# from pykeop... | 37,369 | 32.515695 | 114 | py |
NMTGMinor | NMTGMinor-master/onmt/models/speech_recognizer/mssm/fft_convolution.py | import torch
| 14 | 4 | 12 | py |
NMTGMinor | NMTGMinor-master/onmt/models/speech_recognizer/mssm/ssm_kernel/ssm_kernel_coefficient.py | #!/usr/bin/env python3
import torch
from opt_einsum import contract
import os
import pathlib
import ssm_kernel_coefficient_cuda
# from torch.utils.cpp_extension import load
# ssm_kernel_coefficient_binding = load(
# name="ssm_kernel_coefficient_binding",
# sources=[
# os.path.join(
# path... | 6,529 | 35.077348 | 102 | py |
NMTGMinor | NMTGMinor-master/onmt/models/speech_recognizer/mssm/ssm_kernel/setup.py | import torch
from torch.utils import cpp_extension
from setuptools import setup, find_packages
import subprocess
from pathlib import Path
import sys
import warnings
import os
from torch.utils.cpp_extension import CUDAExtension
from torch.utils.cpp_extension import BuildExtension
from torch.utils.cpp_extension import ... | 5,691 | 34.798742 | 101 | py |
NMTGMinor | NMTGMinor-master/onmt/models/speech_recognizer/fairseq_wav2vec2/wavlm_modules.py | # --------------------------------------------------------
# WavLM: Large-Scale Self-Supervised Pre-training for Full Stack Speech Processing (https://arxiv.org/abs/2110.13900.pdf)
# Github source: https://github.com/microsoft/unilm/tree/master/wavlm
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [se... | 18,345 | 39.320879 | 186 | py |
NMTGMinor | NMTGMinor-master/onmt/models/speech_recognizer/fairseq_wav2vec2/base_fairseq.py | 0 | 0 | 0 | py | |
NMTGMinor | NMTGMinor-master/onmt/models/speech_recognizer/fairseq_wav2vec2/enum.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from enum import Enum, EnumMeta
from typing import List
class StrEnumMeta(EnumMeta):
# this is workaround for submitit pickling leading ... | 1,753 | 31.481481 | 107 | py |
NMTGMinor | NMTGMinor-master/onmt/models/speech_recognizer/fairseq_wav2vec2/adapter.py | import torch
import torch.nn.functional as F
import torch.nn as nn
from onmt.modules.layer_norm import LayerNorm
class Adapter(torch.nn.Module):
def __init__(self, input_dim, downsample_factor=2):
self.input_dim = input_dim
self.middle_dim = input_dim // downsample_factor
super(Adapter, s... | 3,345 | 34.595745 | 112 | py |
NMTGMinor | NMTGMinor-master/onmt/models/speech_recognizer/fairseq_wav2vec2/utils.py | try:
from collections.abc import Iterable
except ImportError:
from collections import Iterable
from omegaconf import DictConfig, OmegaConf, open_dict, _utils
import contextlib
import itertools
import logging
import re
import warnings
from typing import Optional, Tuple, Callable, Dict, List, TYPE_CHECKING
from o... | 11,673 | 36.536977 | 120 | py |
NMTGMinor | NMTGMinor-master/onmt/models/speech_recognizer/fairseq_wav2vec2/wav2vec2.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
from dataclasses import dataclass, field
from typing import List, Tuple
import copy
import numpy as np
import torch
import torch.... | 63,460 | 36.440118 | 121 | py |
NMTGMinor | NMTGMinor-master/onmt/models/speech_recognizer/fairseq_wav2vec2/file_io.py | import os
import shutil
from typing import List, Optional
import logging
IOPathManager = None
class PathManager:
"""
Wrapper for insulating OSS I/O (using Python builtin operations) from
iopath's PathManager abstraction (for transparently handling various
internal backends).
"""
@staticmetho... | 4,912 | 28.071006 | 87 | py |
NMTGMinor | NMTGMinor-master/onmt/models/speech_recognizer/fairseq_wav2vec2/wavlm.py | import math
import logging
from typing import List, Optional, Tuple
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from onmt.modules.layer_norm import LayerNorm
from .fairseq_modules import (
Fp32GroupNorm,
Fp32LayerNorm,
GradMultiply,
SamePad,
TransposeLas... | 35,759 | 37.123667 | 234 | py |
NMTGMinor | NMTGMinor-master/onmt/models/speech_recognizer/fairseq_wav2vec2/fairseq_modules.py | import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor, nn
from torch.nn import Parameter
import math
from typing import Dict, Optional, Tuple
import torch
from torch.cuda.amp import custom_fwd, custom_bwd
from onmt.modules.optimized.self_attention_func import self_attn_func, self_attn_compact_... | 41,973 | 39.870497 | 128 | py |
NMTGMinor | NMTGMinor-master/onmt/models/speech_recognizer/fairseq_wav2vec2/dataclass.py | import sys
from dataclasses import _MISSING_TYPE, dataclass, field
from typing import Any, List, Optional, Tuple
from .enum import ChoiceEnum
from .utils import get_activation_fn, get_available_activation_fns
class FairseqDataclass:
"""fairseq base dataclass that supported fetching attributes and metas"""
_n... | 9,957 | 35.07971 | 119 | py |
NMTGMinor | NMTGMinor-master/onmt/models/bayes_by_backprop/__init__.py | 0 | 0 | 0 | py | |
NMTGMinor | NMTGMinor-master/onmt/models/bayes_by_backprop/relative_transformer.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from onmt.models.transformer_layers import PositionalEncoding, PrePostProcessing
from onmt.models.transformer_layers import EncoderLayer, DecoderLayer
from onmt.models.transformers import TransformerEncoder, TransformerDecoder, Transformer, Transformer... | 18,937 | 38.372141 | 114 | py |
NMTGMinor | NMTGMinor-master/onmt/models/bayes_by_backprop/relative_transformer_layers.py | import torch
import torch.nn as nn
import onmt
from onmt.models.transformer_layers import PrePostProcessing, MultiHeadAttention, Linear
from onmt.utils import flip
from onmt.modules.linear import XavierLinear as Linear
from onmt.modules.linear import XavierLinear
from onmt.modules.attention import MultiHeadAttention
f... | 8,703 | 40.447619 | 103 | py |
NMTGMinor | NMTGMinor-master/onmt/models/multilingual_translator/reversible_transformers.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from onmt.models.transformer_layers import PrePostProcessing
from onmt.modules.attention import MultiHeadAttention
from onmt.modules.optimized.relative_self_attention import RelativeSelfMultiheadAttn
from onmt.modules.optimized.encdec_attention import E... | 21,390 | 34.89094 | 113 | py |
NMTGMinor | NMTGMinor-master/onmt/models/multilingual_translator/__init__.py | 0 | 0 | 0 | py | |
NMTGMinor | NMTGMinor-master/onmt/models/multilingual_translator/relative_transformer.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from onmt.models.transformer_layers import PositionalEncoding, PrePostProcessing
from onmt.models.transformer_layers import EncoderLayer, DecoderLayer
from onmt.models.transformers import TransformerEncoder, TransformerDecoder, Transformer, Transformer... | 21,668 | 43.58642 | 130 | py |
NMTGMinor | NMTGMinor-master/onmt/models/multilingual_translator/relative_transformer_layers.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import onmt
from onmt.models.transformer_layers import PrePostProcessing
from onmt.modules.optimized.encdec_attention import EncdecMultiheadAttn
from onmt.modules.optimized.relative_self_attention import RelativeSelfMultiheadAttn
from onmt.modules.opti... | 21,330 | 50.524155 | 115 | py |
NMTGMinor | NMTGMinor-master/onmt/models/discourse/discourse_transformer.py | # Transformer with discourse information
from collections import defaultdict
import onmt
import torch
import torch.nn as nn
import torch.nn.functional as F
from onmt.models.transformers import Transformer, TransformerDecodingState
from onmt.modules.pre_post_processing import PrePostProcessing
from .gate_layer import R... | 10,079 | 42.261803 | 115 | py |
NMTGMinor | NMTGMinor-master/onmt/models/discourse/__init__.py | 0 | 0 | 0 | py | |
NMTGMinor | NMTGMinor-master/onmt/models/discourse/gate_layer.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import onmt
from onmt.modules.pre_post_processing import PrePostProcessing
from onmt.modules.linear import FeedForward
from onmt.modules.linear import XavierLinear as Linear
from onmt.modules.attention import MultiHeadAttention
from onmt.modules.dropou... | 11,711 | 51.756757 | 117 | py |
NMTGMinor | NMTGMinor-master/onmt/models/deltalm/transformer_decoder.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
from typing import Any, Dict, List, Optional
from collections import defaultdict
import torch
import torch.nn as nn
from .module... | 15,616 | 35.832547 | 119 | py |
NMTGMinor | NMTGMinor-master/onmt/models/deltalm/transformer_encoder.py | import math
from typing import Dict, List, Optional
import torch
import torch.nn as nn
# from fairseq.modules import (
# FairseqDropout,
# LayerDropModuleList,
# LayerNorm,
# PositionalEmbedding,
# SinusoidalPositionalEmbedding,
# )
from .modules.positional_embeddings import PositionalEmbedding, Si... | 7,894 | 33.627193 | 111 | py |
NMTGMinor | NMTGMinor-master/onmt/models/deltalm/__init__.py | 0 | 0 | 0 | py | |
NMTGMinor | NMTGMinor-master/onmt/models/deltalm/deltalm.py | import os
from typing import Any, Dict, List, Optional, Tuple
import torch
import torch.nn as nn
from torch import Tensor
from .transformer_encoder import TransformerEncoderBase
from .transformer_decoder import TransformerDecoderBase
from .modules.transformer_layer import TransformerDecoderLayerBase
from .modules.ut... | 17,669 | 34.841785 | 103 | py |
NMTGMinor | NMTGMinor-master/onmt/models/deltalm/modules/multihead_attention.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
from typing import Dict, Optional, Tuple
import torch
import torch.nn.functional as F
# from fairseq import utils
# from fairseq.... | 19,975 | 40.272727 | 90 | py |
NMTGMinor | NMTGMinor-master/onmt/models/deltalm/modules/efficient_adapters.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from .utils import get_activation_fn
from onmt.modules.layer_norm import layer_norm_func
from onmt.modules.optimized.linear import Linear as LinearModule
def Linear(in_features, out_features, bias=True):
m = LinearModule(in_features, out_features... | 4,610 | 38.75 | 96 | py |
NMTGMinor | NMTGMinor-master/onmt/models/deltalm/modules/positional_embeddings.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
from typing import Dict, Optional, Any
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tenso... | 6,620 | 36.619318 | 94 | py |
NMTGMinor | NMTGMinor-master/onmt/models/deltalm/modules/utils.py | try:
from collections.abc import Iterable
except ImportError:
from collections import Iterable
import contextlib
import itertools
import logging
import re
import warnings
from typing import Optional, Tuple, Callable, Dict, List, TYPE_CHECKING
import numpy as np
import torch
def gelu_accurate(x):
if not ha... | 1,520 | 24.779661 | 91 | py |
NMTGMinor | NMTGMinor-master/onmt/models/deltalm/modules/transformer_layer.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Dict, List, Optional
import torch
import torch.nn as nn
# from fairseq import utils
# from onmt.models.speech_recognizer.f... | 14,593 | 35.303483 | 95 | py |
NMTGMinor | NMTGMinor-master/onmt/models/deltalm/modules/activation_functions.py | import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor, nn
from torch.nn import Parameter
import math
from typing import Dict, Optional, Tuple
import torch
def gelu_accurate(x):
if not hasattr(gelu_accurate, "_a"):
gelu_accurate._a = math.sqrt(2 / math.pi)
return (
0... | 507 | 27.222222 | 91 | py |
NMTGMinor | NMTGMinor-master/onmt/models/deltalm/modules/layer_drop.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
LayerDrop as described in https://arxiv.org/abs/1909.11556.
"""
import torch
import torch.nn as nn
class LayerDropModuleList(nn.ModuleLi... | 1,408 | 31.022727 | 71 | py |
NMTGMinor | NMTGMinor-master/onmt/metrics/gleu.py | # -*- coding: utf-8 -*-
# Natural Language Toolkit: GLEU Score
#
# Copyright (C) 2001-2017 NLTK Project
# Authors:
# Contributors:
# URL: <http://nltk.org/>
# For license information, see LICENSE.TXT
""" GLEU score implementation. """
from __future__ import division
from collections import Counter
from nltk.util impo... | 10,546 | 44.461207 | 126 | py |
NMTGMinor | NMTGMinor-master/onmt/metrics/hit.py | from onmt.metrics.gleu import sentence_gleu
import math
# hit is the metrics for getting rare words copied
class HitMetrics(object):
def __init__(self, alpha=0.5):
self.alpha = alpha
def hit(self, reference, hypothesis):
index = -1;
alpha=self.alpha
for i in ra... | 1,396 | 27.510204 | 102 | py |
NMTGMinor | NMTGMinor-master/onmt/metrics/bleu.py | # -*- coding: utf-8 -*-
# Copyright 2017 Google Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law ... | 2,481 | 29.268293 | 79 | py |
NMTGMinor | NMTGMinor-master/onmt/metrics/__init__.py | from onmt.metrics.bleu import *
from onmt.metrics.gleu import *
from onmt.metrics.sbleu import sentence_bleu
# For flake8 compatibility.
__all__ = []
| 151 | 20.714286 | 44 | py |
NMTGMinor | NMTGMinor-master/onmt/metrics/sbleu.py | import sys
import math
ngramLength = 4;
smoothingConstant=0.1
bpSmoothingConstant=1.5
def getCounts(words):
counts = {}
for i in range(len(words)):
ngram = []
for j in range(ngramLength):
if(i+j < len(words)):
ngram.append(words[i+j])
if(" ".join(ngr... | 3,215 | 26.487179 | 127 | py |
NMTGMinor | NMTGMinor-master/onmt/reversible_models/reversible.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from onmt.models.transformer_layers import PrePostProcessing
from onmt.modules.linear import FeedForward
from onmt.modules.attention import MultiHeadAttention
from torch.autograd.function import Function
import sys
from torch.utils.checkpoint import get... | 1,099 | 34.483871 | 106 | py |
NMTGMinor | NMTGMinor-master/onmt/reversible_models/relative_transformers.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from onmt.models.transformer_layers import PrePostProcessing
# from onmt.modules.linear import FeedForward as position_wise_feed_forward
from onmt.modules.attention import MultiHeadAttention
# from onmt.modules.relative_attention import RelPartialLearna... | 22,745 | 35.865478 | 115 | py |
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